Due to their intrinsic nature, financial institutes are always exposed to various types of risks. Unfortunately, if these risks are not properly managed it can lead to severe consequences that can impact its customers adversely. Post- 2008 recession, the finance sector became much more cautious of such risks and started implementing more robust measures and processes for risk management. In the mid-2010s, the world saw the rise of artificial intelligence technology and this turned to be a game-changer for the finance industry to manage risk by leveraging AI.
In this post, we will see what are the most common risks in the finance industry and how artificial intelligence is helping to mitigate those risks more efficiently.
Finance Risk Management with AI
Credit Risk
This risk is associated with default on credit or loans that banks provide. Typically this happens when credit score of people are not assessed properly and such loans/credits have to be written off, resulting in losses for banks. In March of 2019, the credit default rate was hovering around 3.68% as per the report produced by S&P/Experian.
By building machine learning predictive models with the customer’s data, banks can assess the creditworthiness of new loan or credit card applicants and the probability that he or she will default loan in the future. With these future insights from AI, banks can avoid issuing loans to these individuals and minimize the default. FinTech startup Zest Finance, founded in 2009 has been a pioneer of using machine learning analytics to reduce credit risk and has been able to reduce default rates by 20%.
Market Risk
The market is very volatile and considered to be the most risk-prone avenues within the financial world. A seemingly positive stock market can come crashing down very quickly due to unfavorable conditions. There are too many macro and micro factors that can influence the market trend and it is beyond human capability to evaluate all these factors to manually assess the trend with accuracy.
This is where time-series data modeling can help in forecasting market trends where portfolio managers or individuals can take assess the market better and take action to minimize the risk in case of the possible market decline or crash. Some prominent companies in this space include Trading Technologies and Kavout, who churn out a huge amount of data to identify complex trading patterns to predict the market.
Researches are also going on how to make use of sentiment analysis of news feed to predict the stock market trend in advance. IBM Watson powered company, EquBot gathers data from various social posts and online news to build a cause and effect view of the market.
Operation Risk
This risk is associated with internal operations and management decisions of the finance companies. Poor management decisions can not only be catastrophic to the organizations but it can also put customers in jeopardy. The collapse of RBS in 2008 is attributed to many poor decisions made by the bank. If the bank would not have been rescued, it could have resulted in another Lehman Brothers.
Finance companies are now investing in data scientists to understand the hidden value of their data with the help of machine learning modeling and advance analytics. Equipped with these valuable insights from data, the business leaders can take correct informed decisions for their companies and customers.
Fraud Risk
Finance institutes like banks and insurance are vulnerable to fraud. In the case of banks, most of these frauds are with respect to fraudulent transactions by stolen identity, cards, or other information of customers. In 2019, there were 270,00 cases of credit card fraud reported.
On the other hand, the insurers also have to be wary of fraudulent claims that can result in huge losses. In 2019, the US bases insurers had to face a loss of $34 billion due to fraud claims.
Banks are now using sophisticated and fast AI models that have the capability to flag suspicious online or ATM transactions in realtime and block it.. Datavisor, who specialize in providing AI-based fraud detection products claim that their solution can detect 30% more financial frauds with 90% accuracy
Insurance companies like AKSigorta are making use of machine learning classification models, to flag any suspicious fraud claims that need more verification with great success.
Besides this, prominent banks like RBS, Wells Fargo, Bank of America, Barclays are implementing biometric authentication on their applications to make sure it becomes more difficult to steal their customer’s identities
Insurance Underwriting
In insurance, underwriting is the process to evaluate the premium of policy and its benefit for the insurance plans for specific customer segments or individuals or businesses. This is generally done by evaluating the risk profile of the insured and the likelihood that they will seek claims. If the underwriting is process is not done properly, it can lead to Insurers shelling out more money in claims than they are getting in premiums, resulting in its collapse and leaving other policyholders in a difficult situation.
Insurers are now using artificial intelligence in the underwriting process to assess all the data points for profiling the associated risk and fix the policy plan and premium accordingly. As per McKinsey, by 2030, manual underwriting for personal or small business will cease to exists due to the adoption of artificial intelligence.
Conclusion
Finance companies have managed to use artificial intelligence for risk management very efficiently and successfully. With more breakthroughs happening in the world of AI, it will be very interesting to see how finance companies will put their innovation cap to minimize the risks further across their verticals.
Disclaimer: The author of this text, Robin Trehan, has an undergraduate degree in Economics, Masters in international business and finance, and MBA in electronic business. Trehan is Senior VP at Deltec International deltecbankstag.wpengine.com. The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries, and/or employees.
